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Combining instance weighting and fine tuning for training Naïve Bayesian classifiers with scant training data
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 15, Issue 6 (30 Nov. 2018), pp.1099-1106, 8 p.
Publisher
Publication Date
2018-11-30
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
-This work addresses the problem of having to train a Naïve Bayesian classifier using limited data.
It first presents an improved instance-weighting algorithm that is accurate and robust to noise and then it shows how to combine it with a fine tuning algorithm to achieve even better classification accuracy.
Our empirical work using 49 benchmark data sets shows that the improved instance-weighting method outperforms the original algorithm on both noisy and noise-free data sets.
Another set of empirical results indicates that combining the instance-weighting algorithm with the fine tuning algorithm gives better classification accuracy than using either one of them alone.
American Psychological Association (APA)
al-Hindi, Khalil. 2018. Combining instance weighting and fine tuning for training Naïve Bayesian classifiers with scant training data. The International Arab Journal of Information Technology،Vol. 15, no. 6, pp.1099-1106.
https://search.emarefa.net/detail/BIM-874042
Modern Language Association (MLA)
al-Hindi, Khalil. Combining instance weighting and fine tuning for training Naïve Bayesian classifiers with scant training data. The International Arab Journal of Information Technology Vol. 15, no. 6 (Nov. 2018), pp.1099-1106.
https://search.emarefa.net/detail/BIM-874042
American Medical Association (AMA)
al-Hindi, Khalil. Combining instance weighting and fine tuning for training Naïve Bayesian classifiers with scant training data. The International Arab Journal of Information Technology. 2018. Vol. 15, no. 6, pp.1099-1106.
https://search.emarefa.net/detail/BIM-874042
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 1105-1106
Record ID
BIM-874042